TY - JOUR
T1 - Demand-Oriented Fog-RAN Slicing With Self-Adaptation via Deep Reinforcement Learning
AU - Li, Xuanheng
AU - Jiao, Kajia
AU - Chen, Xingyun
AU - Ding, Haichuan
AU - Wang, Jie
AU - Pan, Miao
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - As one of the key technologies for the 6G system, network slicing can enable the concurrent provisioning of heterogeneous quality of services (QoS) on various types of services under one Fog-RAN architecture. However, effective slicing of Fog-RAN is very challenging due to the diversification of QoS requirements and demand fluctuation. In this article, we propose a demand-oriented two-tier Fog-RAN slicing trading framework to facilitate the slice generation between the mobile network operator (MNO) and service providers (SPs), and also the slice adaptation among SPs with the consideration of three service types, including data transmission, computation offloading and content caching. In Tier-I, a slice generation mechanism is developed to enable the MNO to provide customized slices for SPs by jointly scheduling 3D resources (communication, computing and caching resources) in a large time scale. In Tier-II, a slice adaptation mechanism is designed based on the deep reinforcement learning approach to facilitate SPs to perform effective resource adjustment on their own slices by purchasing/selling resources from/to other SPs in a small time scale. Numerical results show that the proposed scheme can satisfy the various QoS requirements through the 3D resource scheduling, and also improve the resource utilization owing to the proactive slice adaptation. Comparing with the traditional semi-dynamic slicing manner, the adaptive adjustment could improve the resource utilization about 30%.
AB - As one of the key technologies for the 6G system, network slicing can enable the concurrent provisioning of heterogeneous quality of services (QoS) on various types of services under one Fog-RAN architecture. However, effective slicing of Fog-RAN is very challenging due to the diversification of QoS requirements and demand fluctuation. In this article, we propose a demand-oriented two-tier Fog-RAN slicing trading framework to facilitate the slice generation between the mobile network operator (MNO) and service providers (SPs), and also the slice adaptation among SPs with the consideration of three service types, including data transmission, computation offloading and content caching. In Tier-I, a slice generation mechanism is developed to enable the MNO to provide customized slices for SPs by jointly scheduling 3D resources (communication, computing and caching resources) in a large time scale. In Tier-II, a slice adaptation mechanism is designed based on the deep reinforcement learning approach to facilitate SPs to perform effective resource adjustment on their own slices by purchasing/selling resources from/to other SPs in a small time scale. Numerical results show that the proposed scheme can satisfy the various QoS requirements through the 3D resource scheduling, and also improve the resource utilization owing to the proactive slice adaptation. Comparing with the traditional semi-dynamic slicing manner, the adaptive adjustment could improve the resource utilization about 30%.
KW - Deep reinforcement learning
KW - Fog-RAN
KW - network slicing
KW - slice adaptation
KW - slice generation
UR - http://www.scopus.com/inward/record.url?scp=85161071314&partnerID=8YFLogxK
U2 - 10.1109/TVT.2023.3280242
DO - 10.1109/TVT.2023.3280242
M3 - Article
AN - SCOPUS:85161071314
SN - 0018-9545
VL - 72
SP - 14704
EP - 14716
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 11
ER -